173 research outputs found
A review of smartphones based indoor positioning: challenges and applications
The continual proliferation of mobile devices has encouraged much effort in
using the smartphones for indoor positioning. This article is dedicated to
review the most recent and interesting smartphones based indoor navigation
systems, ranging from electromagnetic to inertia to visible light ones, with an
emphasis on their unique challenges and potential real-world applications. A
taxonomy of smartphones sensors will be introduced, which serves as the basis
to categorise different positioning systems for reviewing. A set of criteria to
be used for the evaluation purpose will be devised. For each sensor category,
the most recent, interesting and practical systems will be examined, with
detailed discussion on the open research questions for the academics, and the
practicality for the potential clients
Practical server-side WiFi-based indoor localization: Addressing cardinality & outlier challenges for improved occupancy estimation
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
A two phase framework for visible light-based positioning in an indoor environment: performance, latency, and illumination
Recently with the advancement of solid state lighting and the application thereof
to Visible Light Communications (VLC), the concept of Visible Light Positioning
(VLP) has been targeted as a very attractive indoor positioning system (IPS) due to
its ubiquity, directionality, spatial reuse, and relatively high modulation bandwidth.
IPSs, in general, have 4 major components (1) a modulation, (2) a multiple access
scheme, (3) a channel measurement, and (4) a positioning algorithm. A number of
VLP approaches have been proposed in the literature and primarily focus on a fixed
combination of these elements and moreover evaluate the quality of the contribution
often by accuracy or precision alone.
In this dissertation, we provide a novel two-phase indoor positioning algorithmic
framework that is able to increase robustness when subject to insufficient anchor luminaries
and also incorporate any combination of the four major IPS components.
The first phase provides robust and timely albeit less accurate positioning proximity
estimates without requiring more than a single luminary anchor using time division
access to On Off Keying (OOK) modulated signals while the second phase provides a
more accurate, conventional, positioning estimate approach using a novel geometric
constrained triangulation algorithm based on angle of arrival (AoA) measurements.
However, this approach is still an application of a specific combination of IPS components.
To achieve a broader impact, the framework is employed on a collection
of IPS component combinations ranging from (1) pulsed modulations to multicarrier
modulations, (2) time, frequency, and code division multiple access, (3) received signal
strength (RSS), time of flight (ToF), and AoA, as well as (4) trilateration and
triangulation positioning algorithms.
Results illustrate full room positioning coverage ranging with median accuracies
ranging from 3.09 cm to 12.07 cm at 50% duty cycle illumination levels. The framework
further allows for duty cycle variation to include dimming modulations and results
range from 3.62 cm to 13.15 cm at 20% duty cycle while 2.06 cm to 8.44 cm at a
78% duty cycle. Testbed results reinforce this frameworks applicability. Lastly, a
novel latency constrained optimization algorithm can be overlaid on the two phase
framework to decide when to simply use the coarse estimate or when to expend more
computational resources on a potentially more accurate fine estimate.
The creation of the two phase framework enables robust, illumination, latency
sensitive positioning with the ability to be applied within a vast array of system
deployment constraints
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
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